arXiv:2611.05887v1 Announce Type: new Abstract: Optimization is fundamental to wireless communications, from characterizing channel capacity and designing transmission schemes to allocating network resources. However, conventional workflows require substantial expertise and manual effort, particularly for high-dimensional or nonconvex problems, black-box objectives, and interdependent decisions across time scales. Recent advances in agentic artificial intelligence (AI), particularly agents based on large language models (LLMs), offer new opportunities to address these challenges by integrating reasoning, tool use, and numerical feedback. This article presents an overview of agentic AI for wireless optimization, focusing on workflow automation, algorithm design, and multi-timescale network operations. First, LLM-based agents streamline optimization workflows by automating problem formulation, code generation, numerical solution, and result verification in a closed loop. Second, they facilitate algorithm design by combining and configuring existing numerical methods and refining learning-based optimizers, using numerical feedback to improve solution quality and computational efficiency for nonconvex and black-box problems. Third, they support multi-timescale network operations by decomposing coupled problems into interdependent subtasks and coordinating their solutions to satisfy network-level objectives and service requirements. A multicast beamforming case study demonstrates performance gains from agent-developed solvers, particularly in execution time, and a network energy-saving example illustrates how agents coordinate dynamic base station activation, user association, and beamforming across time scales. Finally, we discuss open challenges and research opportunities in agentic wireless optimization.